isko 2010marianne lykke royal school of library and information science susan l. price and lois m....

ISKO 2010 Marianne Lykke

Marianne LykkeRoyal School of Library and Information Science

Susan L. Price and Lois M. L. DelcambrePortland State University

ISKO 2010 ConferenceSapienza University of Rome, Faculty of Philosophy

February 23 - 26, 2010

Using semantic components to represent and search domain-specific documents: An evaluation of indexing accuracy and consistency


Agenda

• Problem and motivation

• Semantic component model

• Research questions

• Test design

• Results

• Conclusions


Problem and motivation

Challenges for information retrieval in domain-specific digital libraries:

• Domain-specific libraries often contain large sets of similar documents about few topics

o Important to be able to distinguish between topical similar documents

• Domain experts often have specific information needs targeting a single “right answer”, specified by domain-specific facets.

o Important to be able to limit search to domain-specific dimensions

(e.g. Leckie et al., 1996; Fagin et al., 2003; Freund et al., 2005; Hearst et al., 2006)


Problem and motivation

• Little time for information retrieval

o Important that then relevant documents are highly ranked and retrieved by first query

• Distributed indexing, carried out by indexers with varied degree of indexing competenceo Important to address classical indexing problems:

quality, exhaustivity, specificity, consistency (e.g. Leckie et al., 1996; Fagin et al., 2003; Freund et al., 2005; Hearst et al., 2006)


Semantic component model

• Semantic components model developed to facilitate formulation of specific, structured queries covering the search topic exhaustively by domain-specific dimensions

• Two-level model dividing a given collection into a set of document classes, each class with an associated set of semantic components

• Based on assumptions thato Domain experts know document genres within a certain

domain: content and structure (Dillon, 1991; Orlikowski & Yates, 1994; Bishop, 1999; Vaughan & Dillon, 2005)

o Domain-specific document content and structure correspond to domain-specific information needs (Ely et al, 1999,2000; Price, Delcambre, Nielsen, 2006)

HIO 2009 Marianne Lykke

SC: General information

SC: Practical information

Document class: Clinical method


SC: General information

SC: Risk factors

After treatment

Document class: Clinical method


Semantiske component model

Document class Semantic component Document class Semantic component

Clinical problem General informationDiagnosisReferralTreatment

Clinical unit Function and specialtyPractical informationReferralStaff and organization

Clinical method General informationPractical informationReferralAftercareRisksExpected results

Drugs General informationPractical informationTarget groupEffectSide effects

Services General informationPractical informationReferral

Notice General informationPractical informationQualification


Case study

• sundhed.dk: Danish, national health portal

• Active since 2001, 25.000 documents

• Two main target groups: citizens and medical professionals

• Combination of full-text indexing and controlled, assigned indexing: o ICPC, International Classification Primary Careo ICD-10, International Classification of Diseaseso Home-grown Citizens Thesaurus

• Large and varied group of indexers o 5 regionso Up to 250 indexers per region

• Specific target group: family doctors


Test design

• Comparative, experimental indexing studyo Baseline: keyword indexing (controlled and free terms)o Experimental: semantic component indexing

• Test persons: 16 sundhed.dk indexers (convenience sample)

• Indexing task: 12 sundhed.dk documentso 6 documents were indexed with semantic components

(SC)o 6 documents were indexed with keywords

• Random assignment of documents and indexing methods

• Training session• Evaluation measures:

o Accuracy o Consistencyo Indexing timeo Easiness


Research questions

• Is semantic component indexing more accurate than keyword indexing compared to a reference standard?

• Is semantic component indexing more consistent than keyword indexing?

• Is semantic component indexing faster than keyword indexing?

• Is semantic component indexing easier than keyword indexing?


Accuracy

Document Semantic component Keywords

Recall macroaverage

Precisionmacroaverage

Recallmacroaverage

Precision macroaverage

1 0.74 ± 0.37 0.89 ± 0.26 0.14 ± 0.33 0.74 ± 0.43

2 0.56 ± 0.33 0.61 ± 0.39 0.35 ± 0.47 0.74 ± 0.42

3 0.59 ± 0.45 0.72 ± 0.38 0.10 ± 0.23 0.72 ± 0.42

4 0.33 ± 0.29 0.72 ± 0.41 0.16 ± 0.35 0.70 ± 0.45

5 0.74 ± 0.39 0.68 ± 0.47 0.38 ± 0.47 0.85 ± 0.30

6 0.59 ± 0.13 0.81 ± 0.35 0.01 ± 0.04 0.88 ± 0.31

7 0.63 ± 0.39 0.79 ± 0.31 0.28 ± 0.36 0.62 ± 0.41

8 0.70 ± 0.31 0.93 ± 0.17 0.01 ± 0.02 0.61 ± 0.49

9 0.66 ± 0.33 0.76 ± 0.43 0.21 ± 0.39 0.79 ± 0.39

10 0.61 ± 0.35 0.75 ± 0.26 0.25 ± 0.42 0.79 ± 0.39

11 0.65 ± 0.43 0.86 ± 0.31 0.12 ± 0.27 0.80 ± 0.36

12 0.63 ± 0.48 0.83 ± 0.30 0.03 ± 0.08 0.85 ± 0.34


Consistency

Document Semantic component

Keywords

Mean K ± SD(of all semantic

components in the document)

Binary K

(all vocabularies)Traditional 1 ± SD

consistency = c / (a + b – c)

1 0.46 ± 0.35 -0.08 0.05 ± 0.13

2 0.21 ± 0.16 0.001 0.18 ± 0.19

3 0.25 ± 0.30 -0.08 0.05 ± 0.11

4 0.35 ± 0.23 0.02 0.19 ± 0.30

5 0.50 ± 0.30 0.32 0.33 ± 0.23

6 0.05 ± 0.11 -0.07 0.23 ± 0.41

7 0.40 ± 0.48 0.26 0.27 ± 0.18

8 0.66 ± 0.11 -0.08 0.05 ± 0.11

9 0.04 ± 0.24 -0.02 0.09 ± 0.14

10 0.44 ± 0.16 0.27 0.29 ± 0.13

11 0.48 ± 0.41 -0.06 0.04 ± 0.09

12 0.01 ± 0.07 -0.12 0.08 ± 0.24

Time to index

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Easiness

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Conclusions

• Varied accuracy for both indexing methods, but data suggests that semantic component indexing might be more accurate

• Indications that feasibility and easiness of indexing methods are similar

• Semantic component indexing may be preferable alternative if no appropriate controlled vocabulary is available due to short time for development and easy customization to specific document collection

• Limitations:o Small sample and a single domaino Not directly comparable evaluation measure

• Retrieval test shows improvement of document ranking of 25.6% by nDCG (normalized Discounted Cumulative Gain)


Future research

• Development of model:

o Simpler version

o Up-marking by users (social tagging)

o Automatic up-marking

o Up-marking by XML

• Larger scale evaluation

• Evaluation in other domains


Litteratur

Dillon, M (1991). Reader’s model of text structures: the case of academic articles. International Journal of Man-Machine Studies, 35. 913 – 925.

Ely, J, Osheroff, J, Ebell, M, Bergus, G, Levy, B Chambliss, M & Evans, E (1999). Analysis of wquestions asked by family doctors regarding patient care. BMJ, 310 (7206). 358 – 361.

Ely, J, Osheroff, J, Gorman, P, Ebell, M, Bergus, G, Levy, B Chambliss, M, Pifer, E & Stavri, P (2000). A taxonomy of generic clinical questions: classification study. BMJ, 321 (7278). 429 - 432.

Fagin, R., Kumar, R., McCurley, K S., Novak, J., Sivakumar, D., Tomlin, J.A. & Williamson, D.P. (2003). Searching the workplace web. In: Proceedings of the 12th International World Wide Web Conference (WWW ’03), Budapest, Hungary, May 20-24, 2003. 366-375.

Freund, L., Toms, E. & Waterhouse, J. (2005). Modeling the information behaviour of software engineers using a work-task framework. In: Grove, A (ed.) ASIS&T ’05 Proceedings of the 68th Annual meeting, Charlotte, NC, October 28-ember 2, 2005.

Hearst, M & Plaunt, C (1993). Subtopic structuring for full length document access. Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval. 59 – 69.

Leckie, G.J., Pettigrew, K.E. & Sylvain, C. (1996). Modeling the information seeking of professionals. Library Quarterly, 66 (2). 161-193.

Orlikowaki, W J & Yates, J (1994). Genre repertoire: the structuring of communicative practices in organizations. Administrative Science Quarterly, 39. 541 – 574.

Price, S, Delcambre, L & Nielsen, M L (2006). Using semantic components to express questions against document collections. Proceedings International Workshop on Health Information and Knowledge Management (HIKM 2006), Arlington (VA).

Price, S, Nielsen, M L, Delcambre, L & Vedsted, P (2007). Semantic components enhance retrieval of domain-specific documents. Proceedings of the ACM Sixteenth Conference on Information and Knowledge Management (CIKM), Lisboa, November 6 - 8, 2007.


Search term should appear in specified semantic component

Search term


Semantic component should appear in document

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Total Documents

Indexed (max = 96)

Mean Num. Docs Indexed

Per Indexer (max = 6)

Mean Time (min:sec)

Min Time (min:sec)

Max Time (min:sec)

Semantic Components

83 5.2 07:03 00:24 27:05

Keywords 88 5.5 05:56 01:06 31:26

Time required for indexing documents

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Task type

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Prefer keyword indexing About the same Prefer semantic component indexing


Research team

General practice Peter VedstedMD, Ph.D.Research Unit general Practice,Århus University

Jens RubakMDPraksis.dk, Region Midt

Information and computer science

Lois Delcambre, Ph.D., ProfessorSusan Price, MD, Ph.D. studentComputer Science DepartmentPortland State University, USA

Marianne Lykke, Ph.D., Associate professorInformation Interaktion and Information ArkitectureDanmarks Bibliotekskole

sundhed.dk Vibeke Luk Frans la CourInformation specialist IT consultantsundhed.dk Autonomy

Supported by grants from the National Science Foundation, grant numbers 0514238, 0511050 and 0534762, the National Library of

Medicine Training Grant 5-T15-LM07088 and Kvalitetsudviklingsudvalget for Almen Praksis, Aarhus Amt

isko 2010marianne lykke royal school of library and information science susan l. price and lois m....

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